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8th Southern Power Electronics Conference and the 17th Brazilian Power Electronics Conference, SPEC / COBEP 2023

作者： De Carvalho Neto, João T. Salazar, Andrés O. Federal Institute of Education Science and Technology of Rio Grande Do Norte Natal-East Side Campus RN Natal Brazil Federal University of Rio Grande Do Norte Department of Computing Engineering and Automation RN Natal Brazil

ISBN: (纸本)9798350321128

In recent years, the use of photovoltaic systems has getting more attention because radiation is converted directly into electricity without the use of inverters. A major scientific challenge in this theme consists of clean energy storage systems, widely researched for charging electric vehicles (EVs). Batteries are one of the most common ways to store the generated energy directly from photovoltaic systems. Current literature proposes several challenges in charging batteries using photovoltaic systems, especially when it comes to increasing their lifetime, since sudden changes in irradiation cause voltage overshoots in the batteries. This article presents a control strategy based in One-Cycle Control to prevent damages in the batteries adapting its voltage to varying input voltages. The main purpose of this paper is to present applicability of One-Cycle Control based control scheme used for battery charging. The experimental results present the power and controller signal analysis in steady state conditions. © 2023 IEEE.

关键词： Battery Charging One-Cycle Control Photovoltaic Energy

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An Efficient Classification Techniques for Brain Tumor Using Features Extraction and Statistic Methods, with Machine Learning Algorithms

An Efficient Classification Techniques for Brain Tumor Using...

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作者： Badshah, Shah Hussain Farhatullah khan, Gul Zaman Hassan, Muhammad Abul Junaid, Hazrat Sohail, Muhammad Mahbob, Muhammad Awais Ahamad, Izaz Ullah, Nadeem Department of Computing and Technology Abasyn University Peshawar Pakistan School of Automation China University of Geosciences Wuhan430074 China Department of Computer Software Engineering University of Engineering and Technology Mardan Pakistan Department of Computer Science and Information Technology University of Malakand Dir Lower Pakistan Department of Computer Science Bahria University Islamabad Pakistan Department of Computer Science University of Engineering and Technology Taxila Pakistan Department of Information Engineering and Computer Science University of Trento Trento38122 Italy

Today, brain tumor is a very dangerous disease that can even cause death. There are many ways to classify Brain MRI images of tumors. Various aspects of current research have limitations;some methods are accurate but take a long time to compute while some algorithms are fast, but with low accuracy. Consequently, a lot of effort is needed in this area. In this paper, our major goal is to reduce computation time and improve the classification accuracy for brain MRI images. This research proposes four phases: preprocessing, feature extraction, feature selection, and classification. During pre-processing, the median filter was used to reduce noise in the images, and the grayscale image size was reduced to further limit the image for efficient use. After that the Gray images are then given as inputs to the Feature extraction phase to select a limited feature from the image namely, texture features, shape features, smoothing features. The images features obtained in the feature’s extraction phase are still large, and we cannot simply feed them to machine learning models for processing due to computational constraints. So, we need to analyze it and pick out some interesting elements from the images. The statistical features namely, variances, mean, correlation are used in the feature selection phase to select more significant features to reduce the size of the features and reduce computational time. These features are combined and labeled in a file to train Machine Learning (ML) algorithms. We used two types of machine learning algorithms in the classification phase: classifier and evolutionary algorithm. The selected classifier algorithms results are combined in a file and we named it S-K-E-L Classifier and it gives us acceptable results in terms of accuracy and time. Similarly, an evolutionary algorithm named P-S-D-G Classifier also gives us acceptable results in terms of accuracy and time. Lastly, we combine both S-K-E-L Classifier and P-S-D-G Classifier in a file with

关键词： Classification (of information)

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Text Recognition for Station Equipment Troubleshooting Based on BERT-BiLSTM-CRF Models

Text Recognition for Station Equipment Troubleshooting Based...

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2023 China automation Congress, CAC 2023

作者： Bai, Wei Wang, Xiaosu Liang, Bo Yang, Guoyuan Zhi, Hanyu Qiao, Yinwei Institute of Computing Technology China Academy of Railway Sciences Co. Ltd. Beijing China Postgraduate Department China Academy of Railway Sciences Beijing China School of Automation and Software Engineering Shanxi University Taiyuan China

ISBN: (纸本)9798350303759

The excessive length and high semantic complexity of station equipment fault handling report texts are addressed to the extent that effective text recognition is difficult to achieve. A station equipment fault handling dataset is formulated and a text recognition system for station fault reports based on the BERT-BiLSTM-CRF model is designed. The method first preprocesses the data using the BERT pre-trained language model to generate word vectors, which are then fed into BiLSTM to obtain the embedded contextual information, which is passed through the CRF model to obtain the global optimal solution, and finally outputs the textual results of the station equipment fault handling. The experimental results show that the model outperforms the other five comparative models by more than 2% in terms of precision, recall, and F1 value (checking accuracy) for intelligent recognition over the five entities predicted by the other five comparative models. This brings a new direction of thinking for the analysis of equipment troubleshooting texts in railway stations. © 2023 IEEE.

关键词： Semantics

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Probabilistic Subgoal Representations for Hierarchical Reinforcement learning

arXiv

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arXiv 2024年

作者： Wang, Vivienne Huiling Wang, Tinghuai Yang, Wenyan Kämäräinen, Joni-Kristian Pajarinen, Joni Department of Electrical Engineering and Automation Aalto University Finland Huawei Helsinki Research Center Finland Computing Sciences Tampere University Finland

In goal-conditioned hierarchical reinforcement learning (HRL), a high-level policy specifies a subgoal for the low-level policy to reach. Effective HRL hinges on a suitable subgoal representation function, abstracting state space into latent subgoal space and inducing varied low-level behaviors. Existing methods adopt a subgoal representation that provides a deterministic mapping from state space to latent subgoal space. Instead, this paper utilizes Gaussian Processes (GPs) for the first probabilistic subgoal representation. Our method employs a GP prior on the latent subgoal space to learn a posterior distribution over the subgoal representation functions while exploiting the long-range correlation in the state space through learnable kernels. This enables an adaptive memory that integrates long-range subgoal information from prior planning steps allowing to cope with stochastic uncertainties. Furthermore, we propose a novel learning objective to facilitate the simultaneous learning of probabilistic subgoal representations and policies within a unified framework. In experiments, our approach outperforms state-of-the-art baselines in standard benchmarks but also in environments with stochastic elements and under diverse reward conditions. Additionally, our model shows promising capabilities in transferring low-level policies across different tasks. © 2024, CC BY.

关键词： Reinforcement learning

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Probabilistic subgoal representations for hierarchical reinforcement learning 24

Probabilistic subgoal representations for hierarchical reinf...

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Proceedings of the 41st International Conference on Machine Learning

作者： Vivienne Huiling Wang Tinghuai Wang Wenyan Yang Joni-Kristian Kämäräinen Joni Pajarinen Department of Electrical Engineering and Automation Aalto University Finland Huawei Helsinki Research Center Finland Computing Sciences Tampere University Finland

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PeanutNeRF: 3D Radiance Field for Peanuts

PeanutNeRF: 3D Radiance Field for Peanuts

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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Saeed, Farah Sun, Jin Ozias-Akins, Peggy Chu, Ye Juliet Li, Changying Charlie University of Georgia School of Computing United States University of Georgia Institute of Plant Breeding Genetics and Genomics Department of Horticulture United States University of Florida Department of Agricultural and Biological Engineering United States University of Florida Biosensing Automation and Intelligence Laboratory United States

ISBN: (纸本)9798350302493

Accurate phenotypic analysis can help plant breeders efficiently identify and analyze suitable plant traits to enhance crop yield. While 2D images from RGB cameras are easily accessible, their trait estimation performance is limited due to occlusion and the absence of depth information. On the other hand, 3D data from LiDAR sensors are noisy and limited in their ability to capture very thin plant parts such as peanut plant pegs. To combine the merits of both the 2D and 3D data analysis, the 2D images were used to capture thin parts in peanut plants, and deep learning-based 3D reconstruction using captured 2D images was performed to obtain 3D point clouds with information about the scene from different angles. The neural radiance fields were optimized for implicit 3D representation of the plants. The trained radiance fields were queried for 3D reconstruction to achieve point clouds for a 360-degree view and frontal view of the plant. With frontal-view reconstruction and the corresponding 2D images, we used Frustum PVCNN to perform 3D detection of peanut pods. We showed the effectiveness of PeanutNeRF on peanut plants with and without foliage: it showed negligible noise and a chamfer distance of less than 4 × 10-4 from a manually cleaned version. The pod detection showed a precision of around 0.7 at the IoU threshold of 0.5 on the validation set. This method can assist in accurate plant phenotypic studies of peanuts and other important crops. © 2023 IEEE.

关键词： Crops

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Numerical Analysis of Internal Defect Detection in Aluminum Alloy Pipes Aims to Provid Data Support for AI Models

Numerical Analysis of Internal Defect Detection in Aluminum ...

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2024 lEEE International Conference on Advanced Information, Mechanical engineering, Robotics and automation, AIMERA 2024

作者： Bai, Pengshihan Wang, Shang Long, Jianqiao Zheng, Kexin Ma, Jing Li, Nandu Bruce, Steve University of Science and Technology Beijing School of Metallurgical and Ecological Engineering Beijing China Beijing Polytechnic School of Automotive Engineering Beijing China Newcastle University School of Computing United Kingdom Dezhou Natural Resources Bureau Shandong China Information China University of Geosciences School of Mechanical Engineering and Electronic Wuhan China SEH Automation Pte Ltd Department of Research & Development Singapore

ISBN: (纸本)9798350343335

The online effective detection of internal defects in metals has long faced many challenges. The rapid development of Artificial Intelligence (AI) technology has provided possibilities for improving defect detection techniques. However, AI algorithms rely heavily on large amounts of data, which are difficult to obtain through traditional experimental methods. To address this issue, this study proposes a method for the internal defect detection of aluminum alloy pipes and provides data support for AI models and algorithms. We established a "pinch roller-pressure roller-aluminum alloy pipes"model and obtained the variation rules of Mises stress, constraint reaction force caused by the change of internal defect position through numerical calculation. The results show that the position change of the internal defect has a clear functional relationship with the constraint reaction force of roller-A. When the defect is located near the lower part of roller-B, the constraint reaction force of roller-A reaches the maximum value (978.8 N). This study provides empirical evidence for establishing an online defectdetection AI models. It clarifies the evolution pattern of constraint forces related to crack location. These findings provide data support for developing efficient and accurate defect online detection systems based on AI to address the limitations of traditional methods. © 2024 IEEE.

关键词： Aluminum pipe

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MFANet: Mixed Feature Attention Network for Port Lane Detection 42

MFANet: Mixed Feature Attention Network for Port Lane Detect...

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42nd Chinese Control Conference, CCC 2023

作者： Zhang, Jinwei Su, Jinya Yi, Dewei Yang, Jun Ministry of Education The School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing210096 China University of Aberdeen Department of Computing Science AberdeenAB24 3UE United Kingdom

ISBN: (纸本)9789887581543

With the advancement of industrial automation and artificial intelligence technology, unmanned port autonomy has gained increasing attention. Port automatic driving technology is a critical component of unmanned autonomous systems, enabling rubber tire gantry cranes (RTGs) to autonomously navigate by detecting port lanes and implementing control commands. In this study, a deep learning-based method is proposed for detecting port lanes in complex port scenes. The method employs the Inception module and attention mechanism to enhance lane feature extraction, and a structural loss is introduced to explicitly constrain the detection results and incorporate the prior characteristics of port lanes. A novel Mixed Feature Attention Network (MFANet) is proposed to implement the method for port lane detection. Experimental results demonstrate that MFANet effectively improves the accuracy of port lane detection while maintaining high computational efficiency. Furthermore, the performance of MFANet is evaluated in a real-world application scenario. © 2023 Technical Committee on Control Theory, Chinese Association of automation.

关键词： Computational efficiency

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Survey on Foundation Models for Prognostics and Health Management in Industrial Cyber-Physical Systems

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2024年 2卷 264-280页

作者： Liu, Ruonan Zhang, Quanhu Han, Te Yang, Boyuan Zhang, Weidong Yin, Shen Zhou, Donghua Shanghai Jiao Tong University Department of Automation Shanghai200240 China Tianjin University College of Intelligence and Computing Tianjin300350 China Beijing Institute of Technology Center for Energy and Environmental Policy Research Beijing100081 China Beijing Institute of Technology School of Management Beijing100081 China Beijing Laboratory for System Engineering of Carbon Neutrality Beijing100081 China Nanjing University Center for Advanced Control and Smart Operations Suzhou215163 China Hainan University School of Information and Communication Engineering Haikou570228 China Shanghai Jiaotong University Department of Automation Shanghai200240 China Norwegian University of Science and Technology Trondheim7491 Norway

Industrial Cyber-Physical Systems (ICPS) integrating disciplines such as computer science, communication technology, and engineering, have become a crucial component of modern manufacturing and industry. However, ICPS faces numerous challenges during long-term operation, including equipment faults, performance degradation, and security threats, etc. To achieve efficient maintenance and management, prognostics and health management (PHM) has been widely applied in the critical tasks of ICPS such as fault prediction, health monitoring, and maintenance decision-making. The emergence of large-scale foundation models (LFMs) like BERT and GPT marks a significant advancement in artificial intelligence (AI) technology, demonstrating substantial application potential in multiple fields. The accumulation of AI technology, rapid development of LFMs, and the abundance of industrial data and industrial process knowledge provide the foundational conditions for the construction and advancement of industrial LFMs. However, there is currently a lack of consensus on applying LFMs of PHM in ICPS, necessitating a systematic review and roadmap to clarify future development directions. To bridge this gap, this survey provides a comprehensive survey and understanding of the recent advances in LFMs of PHM in ICPS. It provides valuable references for decision makers and researchers in the industry, and helps to further improve the reliability, availability and safety of ICPS. © 2023 IEEE.

关键词： Cyber Physical System

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Surface Extraction and Boundary Detection Based on DBSCAN Clustering in 3D Point Clouds 16

Surface Extraction and Boundary Detection Based on DBSCAN Cl...

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16th IEEE International Conference on Electronic Measurement and Instruments, ICEMI 2023

作者： Chen, Hui Xie, Tingting Menhas, Muhammad Llyas Ahmad, Bilal Huang, Heping Li, Qilin College of Automation Engineering Shanghai University of Electric Power Shanghai China Mirpur University of Science and Technology Department of Electrical Engineering Mirpur Pakistan College of Electrical Engineering University of Poonch Rawalakot Mirpur Pakistan Zhengtai Instrument Co. Ltd Hangzhou China School of Electrical Engineering Computing and Mathematical Sciences Curtin University Perth Australia

ISBN: (纸本)9798350327144

Surface extraction from point clouds has many applications in product design, reverse engineering, industrial manufacturing and more. One of the key challenges is the detection and segmentation of overlapping areas of different surfaces. Existing methods are mainly on local judgment of similarity features, which can lead to either over- or under-segmentation. The proposed method in this paper utilizes DBSCAN for 3D point clouds surface extraction and boundary detection. Prior to extraction, the normal vector at sharp features is estimated based on a voting method. The surface extraction process is comprises two stages. First, the local quadric surface is determined based on the candidate sample points, and the points belonging to the same local surface are clustered. Second, in the merging stage, where the local surfaces are combined into a single smooth surface using three merging conditions. To demonstrate its effectiveness, the proposed approach is accessed on synthetic and real datasets, showing that the method can address the problem of insufficient and excessive segmentation of curved point clouds, and achieve high extraction accuracy. © 2023 IEEE.

关键词： Merging

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